Citation Network Main Path Identification Based on Associated Attributes of Articles: Case Study from Synthetic Biology
Wei Ling1,2,3, Liu Chunjiang2, Xu Haiyun2, Fang Shu2
1. School of Information and Management, Shanxi University of Finance and Economics, Taiyuan 030006; 2. Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu 610041; 3. University of Chinese Academy of Sciences, Beijing 100049
Abstract State-of-the-art citation network main path analysis methods either treat all citation relationships with equal weightage, or calculate the relevance of citations based on the content of the text. Neither nonequivalence of citation nodes nor relevance of citations based on associated attributes of articles are considered. This study uses meta paths to describe and quantify the relevance of citations based on associated attributes of articles, considering it as part of the main path traversal weight, and combines it with SPC (search path count) to construct two new indices to analyze the contribution of associated attributes of articles to main path identification. The results prove that the new traversal weight indices can identify main path characteristics of associated attributes, reveal knowledge diffusion paths in different views, and provide detailed diffusion information. This work expands the functions and application scenarios of citation network main path analysis.
Wei Ling,Liu Chunjiang,Xu Haiyun, et al. Citation Network Main Path Identification Based on Associated Attributes of Articles: Case Study from Synthetic Biology[J]. 情报学报, 2018, 37(4): 351-361.
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